The role of artificial intelligence in screening and democratizing quality prenatal care: A retrospective validation on sonograms of fetal brain

With the emergence of Artificial Intelligence (AI), assistive technologies leveraging them could be highly beneficial in low-resource and remote settings that lack well-trained clinicians and operators, enabling timely referrals for suspected abnormalities and high-risk pregnancies. In this study, we validated the Origin Health Examination Assistant (OHEA), a software system comprising more than 10 AI algorithms to assist in assessing mid-trimester fetal brain exams (axial; transventricular, and transcerebellar). We retrospectively obtained a test set of 222 singleton exams (543 frozen frame images and 39 2D-cine loops; 75.2 % normal, 24.8% abnormal) from a single tertiary fetal care center. The OHEA analyzed each exam for quality (appropriateness in magnification), and performed an anatomical survey (anatomical landmarks visualized/not visualized; assessing if structurally normal/abnormal for the gestational age). Further, a panel of 2 maternal-fetal-medicine (MFM) specialists selected appropriate images from each exam where OHEA automatically placed the caliper points and obtained key fetal brain measurements. The standard of reference for the study was the consensus from a panel of 7 MFMs who reviewed each of these exams. The OHEA demonstrated an excellent accuracy of 97.3% in the assessment of examination quality, 98.2% in the anatomical survey, and excellent agreement (with respect to the MFM panel; intra-class correlation coefficient > 0.90 for all cases) in obtaining key measurements (BPD, HC, OFD, NFT, TCD, AW). The OHEA achieved an overall screening (classifying as normal and abnormal) performance of 0.95 and 0.80 in sensitivity and specificity. Specifically, OHEA could detect (sensitivity, specificity) choroid plexus cyst (0.97, 0.83), absent cavum septum pellucidum (0.87, 0.71), absent midline falx (1.0, 1.0), enlarged cisterna magna (1.0, 1.0), and dilated lateral cerebral ventricles (0.96, 0.96) with high accuracy. The clinical translation of such assistive technology can help clinicians and operators implement and deliver standardized and high-quality prenatal examinations in low-resource settings. In future studies, we aim to improve clinical performance, test on larger populations, and assess performance on exams obtained by novice users.